Phylogenetic Quantification of Intratumor Heterogeneity

MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution

Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Multiplatform Molecular Profiling Reveals Epigenomic Intratumor Heterogeneity in Ependymoma

Ependymomas exist within distinct genetic subgroups, but the molecular diversity within individual ependymomas is unknown. We perform multiplatform molecular profiling of 6 spatially distinct samples from an ependymoma with C11orf95-RELA fusion. DNA methylation and RNA sequencing distinguish clusters of samples according to neuronal development gene expression programs that could also be delineated by differences in magnetic resonance blood perfusion. Exome sequencing and phylogenetic analysis reveal epigenomic intratumor heterogeneity and suggest that chromosomal structural alterations may precede accumulation of single-nucleotide variants during ependymoma tumorigenesis. In sum, these findings shed light on the oncogenesis and intratumor heterogeneity of ependymoma.

Tumor heterogeneity poses a barrier to cancer treatment. Liu etal. investigate radiographically distinct regions of an ependymoma tumor using transcriptomic, genetic, and epigenomic profiling and discover axes of gene expression programs that recapitulate normal brain development in addition to phylogenies that shed light on the tumorigenesis of ependymoma.

iRODS metadata management for a cancer genome analysis workflow

BACKGROUND

The massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date.

RESULTS

We describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information.

CONCLUSIONS

Integrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.